Low light and thermal image normalization for advanced fusion

ABSTRACT

Techniques for generating a fused enhanced image. A first image is generated using a first camera of a first modality, and a second image is generated using a second camera of a second modality. Pixels that are common between the two images are identified. Textures for the common pixels are determined. A camera characteristic, which is linked to noise, is identified. A scaling factor is applied to the textures in the first image. A first saliency is determined using the scaled textures. A second saliency is determined using the textures from the second image. An alpha map is generated and reflects edge detection weights that have been computed for each one of the common pixels based on the two saliencies. Based on the alpha map, textures are merged from the common pixels to generate the fused enhanced image.

BACKGROUND

Mixed-reality (MR) systems, including virtual-reality (VR) andaugmented-reality (AR) systems, have received significant attentionbecause of their ability to create truly unique experiences for theirusers. For reference, conventional VR systems create completelyimmersive experiences by restricting their users' views to only virtualenvironments. This is often achieved through the use of a head-mounteddevice (HMD) that completely blocks any view of the real world. As aresult, a user is entirely immersed within the virtual environment. Incontrast, conventional AR systems create an augmented-reality experienceby visually presenting virtual objects that are placed in or thatinteract with the real world.

As used herein, VR and AR systems are described and referencedinterchangeably. Unless stated otherwise, the descriptions herein applyequally to all types of MR systems, which (as detailed above) include ARsystems, VR reality systems, and/or any other similar system capable ofdisplaying virtual content. Use of the term “HMD” can also refer to a MRsystem.

A MR system can employ different types of cameras (aka “modalities”) inorder to display content to users, such as in the form of a passthroughimage. A passthrough image or view can aid users in avoidingdisorientation and/or safety hazards when transitioning into and/ornavigating within a MR environment. A MR system can present viewscaptured by cameras in a variety of ways. The process of using imagescaptured by world-facing cameras to provide views of a real-worldenvironment provides many advantages. Despite the current benefitsprovided by passthrough images, there are additional benefits that maybe achieved by improving the processes by which passthrough images aregenerated, especially when multiple different cameras are involved.Accordingly, it is desirable to further improve the benefits provided bypassthrough image generation techniques.

The subject matter claimed herein is not limited to embodiments thatsolve any disadvantages or that operate only in environments such asthose described above. Rather, this background is only provided toillustrate one exemplary technology area where some embodimentsdescribed herein may be practiced.

BRIEF SUMMARY

Embodiments disclosed herein relate to systems, devices (e.g., wearabledevices, head mounted devices, hardware storage devices, etc.), andmethods for mitigating the effects of noise in an image when fusingmultiple images together to generate a fused enhanced image.

Some embodiments include a first camera of a first modality (e.g.,perhaps a low light modality) and a second camera of a second modality(e.g., perhaps a thermal imaging modality). The embodiments generate afirst image of an environment using the first camera and generate asecond image of the environment using the second camera. Pixels that arecommon between the two images are identified. A first set of texturesfor the common pixels included in the first image are determined, and asecond set of textures for the common pixels included in the secondimage are also determined. The embodiments also determine a cameracharacteristic (e.g., a gain setting) of the first camera, where thatcharacteristic is linked or associated with noise that may be present inthe first image. Based on the camera characteristic, a scaling factor isapplied to the first set of textures to generate a scaled set oftextures. In effect, applying the scaling factor operates to mitigatenoise that is potentially present in the first image. The embodimentsuse the scaled set of textures to determine a first saliency of thefirst image, where the term “saliency” refers to or reflects an amountof texture variation that is present in the scaled set of textures.Additionally, the embodiments determine a second saliency of the secondimage. The embodiments generate an alpha map that reflects edgedetection weights that have been computed for each one of the commonpixels based on the saliencies. Using the alpha map, the embodimentsmerge textures from the common pixels included in the first image andthe second image to generate a fused enhanced image.

This Summary is provided to introduce a selection of concepts in asimplified form that are further described below in the DetailedDescription. This Summary is not intended to identify key features oressential features of the claimed subject matter, nor is it intended tobe used as an aid in determining the scope of the claimed subjectmatter.

Additional features and advantages will be set forth in the descriptionwhich follows, and in part will be obvious from the description, or maybe learned by the practice of the teachings herein. Features andadvantages of the invention may be realized and obtained by means of theinstruments and combinations particularly pointed out in the appendedclaims. Features of the present invention will become more fullyapparent from the following description and appended claims, or may belearned by the practice of the invention as set forth hereinafter.

BRIEF DESCRIPTION OF THE DRAWINGS

In order to describe the manner in which the above-recited and otheradvantages and features can be obtained, a more particular descriptionof the subject matter briefly described above will be rendered byreference to specific embodiments which are illustrated in the appendeddrawings. Understanding that these drawings depict only typicalembodiments and are not therefore to be considered to be limiting inscope, embodiments will be described and explained with additionalspecificity and detail through the use of the accompanying drawings inwhich:

FIG. 1 illustrates an example head mounted device (HMD).

FIG. 2 illustrates how an HMD can include various different cameras.

FIG. 3 illustrates how an HMD may be used to scan an environment togenerate different images.

FIG. 4 illustrates how different images can include common content andhow each pixel in the image can be assigned a texture value.

FIG. 5 illustrates how variations in texture can be used to determine asaliency of an image, where the saliency reflects detected edges.

FIG. 6 illustrates the results of performing edge detection on an image.

FIG. 7 illustrates different techniques for performing edge detection.

FIG. 8 illustrates different techniques for determining saliency.

FIG. 9 illustrates a generalized flow process for generating an enhancedimage using saliency measures, alpha maps, and edge detection weights.

FIG. 10 illustrates an example equation that may be used to generate analpha map.

FIGS. 11A, 11B, 11C, and 11D illustrate example scenarios in which anenhanced image is generated.

FIG. 12 illustrates an example of a noisy image.

FIG. 13 illustrates the effects of performing edge detection on a noisyimage.

FIG. 14 illustrates an example process in which a scaling factor isapplied to the texture values or intensity values of an image in orderto mitigate the effects of noise in the image.

FIG. 15 shows the result of performing edge detection on a set of scaledtexture values.

FIGS. 16A and 16B illustrate a flow chart of an example method formitigating the effects of noise when fusing multiple images together togenerate a fused enhanced image.

FIG. 17 illustrates an example computer system configured to perform anyof the disclosed operations.

DETAILED DESCRIPTION

Embodiments disclosed herein relate to systems, devices (e.g., wearabledevices, head mounted devices, hardware storage devices, etc.), andmethods for mitigating the effects of noise when fusing multiple imagestogether to generate a fused enhanced image.

Some embodiments include at least two cameras of differing modalities(e.g., a low light camera and a thermal imaging camera). A first imageis generated using a first camera, and a second image is generated usinga second camera. Pixels that are common between the two images areidentified. Textures for the common pixels are determined. A cameracharacteristic (e.g., a gain setting) of the first camera is determined,and, based on that characteristic, a scaling factor is selected andapplied to one set of textures. Saliencies of the two images (one ofwhich is scaled) are determined, where the saliencies reflect amounts oftexture variation present in those images. An alpha map is generated andis configured to reflect edge detection weights that have been computedfor each one of the common pixels based on the two saliencies. Based onthe alpha map, the embodiments merge textures from the common pixels togenerate the enhanced image.

Examples of Technical Benefits, Improvements, and Practical Applications

The following section outlines some example improvements and practicalapplications provided by the disclosed embodiments. It will beappreciated, however, that these are just examples only and that theembodiments are not limited to only these improvements.

The disclosed embodiments bring about substantial benefits to thetechnical field. By way of example, the embodiments are able to produceor generate a so-called “enhanced” image. Different camera modalitiesare designed to provide different types of benefits. By following thedisclosed principles, the embodiments are able to generate an enhancedimage, which enables the benefits that are available to each individualmodality to now be made available via a single image as opposed tomultiple images. In doing so, improved analytics, computer vision, anduser interaction with the computer system are achieved. Furthermore, theuser (in some instances) is provided with content that he/she wouldpotentially not be able to view or interact with if only a single imagetype or modality were used.

Additionally, the disclosed embodiments are able to mitigate the effectsof noise that may be present in an image. When images are fusedtogether, it is possible that noise in one image can detrimentallyimpact the resulting fused image. By following the disclosed principles,the embodiments are able to beneficially eliminate or at least reducethe impact noise might have on the resulting fused enhanced image.Accordingly, these and other benefits will be described in more detailthroughout the remaining portion of this disclosure.

Example HMDs & Scanning Systems

Attention will now be directed to FIG. 1 , which illustrates an exampleof a head-mounted device (HMD) 100. HMD 100 can be any type of MR system100A, including a VR system 100B or an AR system 100C. It should benoted that while a substantial portion of this disclosure is focused onthe use of an HMD to scan an environment to provide a passthroughvisualization (aka passthrough image), the embodiments are not limitedto being practiced using only an HMD. That is, any type of scanningsystem can be used, even systems entirely removed or separate from anHMD. For example, a self-driving car can implement the disclosedoperations.

Consequently, the disclosed principles should be interpreted broadly toencompass any type of scanning scenario or device. Some embodiments mayeven refrain from actively using a scanning device themselves and maysimply use the data generated by the scanning device. For instance, someembodiments may at least be partially practiced in a cloud computingenvironment.

HMD 100 is shown as including scanning sensor(s) 105 (i.e. a type ofscanning or camera system), and HMD 100 can use the scanning sensor(s)105 to scan environments, map environments, capture environmental data,and/or generate any kind of images of the environment (e.g., bygenerating a 3D representation of the environment or by generating a“passthrough” visualization). Scanning sensor(s) 105 may comprise anynumber or any type of scanning devices, without limit.

In accordance with the disclosed embodiments, the HMD 100 may be used togenerate a passthrough visualization of the user's environment. A“passthrough” visualization refers to a visualization that reflects whatthe user would see if the user were not wearing the HMD 100, regardlessof whether the HMD 100 is included as a part of an AR system or a VRsystem, though that passthrough image may be supplemented withadditional or enhanced content. To generate this passthroughvisualization, the HMD 100 may use its scanning sensor(s) 105 to scan,map, or otherwise record its surrounding environment, including anyobjects in the environment, and to pass that data on to the user toview. In many cases, the passed-through data is modified to reflect orto correspond to a perspective of the user's pupils. The perspective maybe determined by any type of eye tracking technique.

To convert a raw image into a passthrough image, the scanning sensor(s)105 typically rely on its cameras (e.g., head tracking cameras, handtracking cameras, depth cameras, or any other type of camera) to obtainone or more raw images of the environment. In addition to generatingpassthrough images, these raw images may also be used to determine depthdata detailing the distance from the sensor to any objects captured bythe raw images (e.g., a z-axis range or measurement). Once these rawimages are obtained, then passthrough images can be generated (e.g., onefor each pupil), and a depth map can also be computed from the depthdata embedded or included within the raw images.

As used herein, a “depth map” details the positional relationship anddepths relative to objects in the environment. Consequently, thepositional arrangement, location, geometries, contours, and depths ofobjects relative to one another can be determined. From the depth maps(and possibly the raw images), a 3D representation of the environmentcan be generated.

Relatedly, from the passthrough visualizations, a user will be able toperceive what is currently in his/her environment without having toremove or reposition the HMD 100. Furthermore, as will be described inmore detail later, the disclosed passthrough visualizations will alsoenhance the user's ability to view objects within his/her environment(e.g., by displaying additional environmental conditions that may nothave been detectable by a human eye).

It should be noted that while the majority of this disclosure focuses ongenerating “a” passthrough image, the embodiments actually generate aseparate passthrough image for each one of the user's eyes. That is, twopassthrough images are typically generated concurrently with oneanother. Therefore, while frequent reference is made to generating whatseems to be a single passthrough image, the embodiments are actuallyable to simultaneously generate multiple passthrough images.

In some embodiments, scanning sensor(s) 105 include visible lightcamera(s) 110, low light camera(s) 115, thermal imaging camera(s) 120,ultraviolet (UV) cameras 125, monochrome 130 cameras, and infraredcamera(s) 135. The ellipsis 140 demonstrates how any other type ofcamera or camera system (e.g., depth cameras, time of flight cameras,etc.) may be included among the scanning sensor(s) 105. In this regard,cameras of different modalities (as reflected by modality 145) areincluded on the HMD 100. The scanning sensor(s) 105 generate images,which may be used to generate passthrough images, which may then bedisplayed on a display 150 of the HMD 100.

In some embodiments, the visible light camera(s) 110 and the low lightcamera(s) 115 (aka low light night vision cameras) operate inapproximately the same overlapping wavelength range. In some cases, thisoverlapping wavelength range is between about 400 nanometers and about1,100 nanometers. Additionally, in some embodiments these two types ofcameras are both silicon detectors.

One distinguishing feature between these two types of cameras is relatedto the illuminance conditions or illuminance range(s) in which theyactively operate. In some cases, the visible light camera(s) 110 are lowpower cameras and operate in environments where the illuminance isbetween about 10 lux and about 100,000 lux, or rather, the illuminancerange begins at about 10 lux and increases beyond 10 lux. In contrast,the low light camera(s) 115 consume more power and operate inenvironments where the illuminance range is between about 110 micro-luxand about 10 lux.

The thermal imaging camera(s) 120, on the other hand, are structured todetect electromagnetic radiation or IR light in the far-IR (i.e.thermal-IR) range, though some embodiments also enable the thermalimaging camera(s) 120 to detect radiation in the mid-IR range. Toclarify, the thermal imaging camera(s) 120 may be a long wave infraredimaging camera structured to detect electromagnetic radiation bymeasuring long wave infrared wavelengths. Often, the thermal imagingcamera(s) 120 detect IR radiation having wavelengths between about 8microns and 14 microns. These wavelengths are also included in the lightspectrum(s). Because the thermal imaging camera(s) 120 detect far-IRradiation, the thermal imaging camera(s) 120 can operate in anyilluminance condition, without restriction.

Accordingly, as used herein, reference to “visible light cameras”(including “head tracking cameras”), are cameras that are primarily usedfor computer vision to perform head tracking. These cameras can detectvisible light, or even a combination of visible and IR light (e.g., arange of IR light, including IR light having a wavelength of about 850nm). In some cases, these cameras are global shutter devices with pixelsbeing about 3 μm in size. Low light cameras, on the other hand, arecameras that are sensitive to visible light and near-IR. These camerasare larger and may have pixels that are about 8 μm in size or larger.These cameras are also sensitive to wavelengths that silicon sensors aresensitive to, which wavelengths are between about 350 nm to 1100 nm.Thermal/long wavelength IR devices (i.e. thermal imaging cameras) havepixel sizes that are about 10 μm or larger and detect heat radiated fromthe environment. These cameras are sensitive to wavelengths in the 8 μmto 14 μm range. Some embodiments also include mid-IR cameras configuredto detect at least mid-IR light. These cameras often comprisenon-silicon materials (e.g., InP or InGaAs) that detect light in the 800nm to 2 μm wavelength range.

Accordingly, the disclosed embodiments may be structured to utilizenumerous different camera modalities. The different camera modalitiesinclude, but are not limited to, visible light or monochrome cameras,low light cameras, thermal imaging cameras, and UV cameras.

It should be noted that any number of cameras may be provided on the HMD100 for each of the different camera types/modalities. That is, thevisible light camera(s) 110 may include 1, 2, 3, 4, 5, 6, 7, 8, 9, 10,or more than 10 cameras. Often, however, the number of cameras is atleast 2 so the HMD 100 can perform stereoscopic depth matching.Similarly, the low light camera(s) 115, the thermal imaging camera(s)120, the UV camera(s) 125, the monochrome 130 cameras, and the infraredcamera(s) 135 may each respectively include 1, 2, 3, 4, 5, 6, 7, 8, 9,10, or more than 10 corresponding cameras.

FIG. 2 illustrates an example HMD 200, which is representative of theHMD 100 from FIG. 1 . HMD 200 is shown as including multiple differentcameras, including cameras 205, 210, 215, 220, and 225. Cameras 205-225are representative of any number or combination of the visible lightcamera(s) 110, the low light camera(s) 115, the thermal imagingcamera(s) 120, the UV camera(s) 125, the monochrome 130 cameras, and theinfrared camera(s) 135 from FIG. 1 . While only 5 cameras areillustrated in FIG. 2 , HMD 200 may include more or less than 5 cameras.

In some cases, the cameras can be located at specific positions on theHMD 200. For instance, in some cases a first camera (e.g., perhapscamera 220) is disposed on the HMD 200 at a position above a designatedleft eye position of any users who wear the HMD 200 relative to a heightdirection of the HMD. For instance, the camera 220 is positioned abovethe pupil 235. As another example, the first camera (e.g., camera 220)is additionally positioned above the designated left eye positionrelative to a width direction of the HMD. That is, the camera 220 ispositioned not only above the pupil 235 but also in-line relative to thepupil 235. When a VR system is used, a camera may be placed directly infront of the designated left eye position. For example, with referenceto FIG. 2 , a camera may be physically disposed on the HMD 200 at aposition in front of the pupil 235 in the z-axis direction.

When a second camera is provided (e.g., perhaps camera 210), the secondcamera may be disposed on the HMD at a position above a designated righteye position of any users who wear the HMD relative to the heightdirection of the HMD. For instance, the camera 210 is above the pupil230. In some cases, the second camera is additionally positioned abovethe designated right eye position relative to the width direction of theHMD. In some cases, the first camera is a low light camera, and the HMDincludes one or more low light cameras. In some cases, the second camerais a thermal imaging camera, and HMD includes one or more thermalimaging cameras. The HMD may additionally include multiple visible lightRGB cameras or monochrome cameras. When a VR system is used, a cameramay be placed directly in front of the designated right eye position.For example, with reference to FIG. 2 , a camera may be physicallydisposed on the HMD 200 at a position in front of the pupil 230 in thez-axis direction.

When a user wears HMD 200, HMD 200 fits over the user's head and the HMD200's display is positioned in front of the user's pupils, such as pupil230 and pupil 235. Often, the cameras 205-225 will be physically offsetsome distance from the user's pupils 230 and 235. For instance, theremay be a vertical offset in the HMD height direction (i.e. the “Y”axis), as shown by offset 240. Similarly, there may be a horizontaloffset in the HMD width direction (i.e. the “X” axis), as shown byoffset 245.

As described earlier, HMD 200 is configured to provide passthroughimage(s) 250 for the user of HMD 200 to view. In doing so, HMD 200 isable to provide a visualization of the real world without requiring theuser to remove or reposition HMD 200. Sometimes, the visualization isenhanced, modified, or supplemented with additional information, as willbe described in more detail later. The passthrough image(s) 250effectively represent the same view the user would see if the user werenot wearing HMD 200. Cameras 205-225 are used to provide thesepassthrough image(s) 250.

None of the cameras 205-225, however, are directly aligned with thepupils 230 and 235. The offsets 240 and 245 actually introducedifferences in perspective as between the cameras 205-225 and the pupils230 and 235. These perspective differences are referred to as“parallax.”

Because of the parallax occurring as a result of the offsets 240 and245, raw images produced by the cameras 205-225 are not available forimmediate use as passthrough image(s) 250. Instead, it is beneficial toperform a parallax correction 255 (aka an image synthesis) on the rawimages to transform the perspectives embodied within those raw images tocorrespond to perspectives of the user's pupils 230 and 235. Theparallax correction 255 includes any number of distortion corrections(e.g., to correct for concave or convex wide or narrow angled cameralenses), epipolar transforms (e.g., to parallelize the optical axes ofthe cameras), and/or reprojection transforms (e.g., to reposition theoptical axes so as to be essentially in front of or in-line with theuser's pupils). The parallax correction 255 may include performing depthcomputations to determine the depth of the environment and thenreprojecting images to a determined location or as having a determinedperspective. As used herein, the phrases “parallax correction” and“image synthesis” may be interchanged with one another and may includeperforming stereo passthrough parallax correction and/or imagereprojection parallax correction.

In some cases, the parallax correction 255 includes a planarreprojection 260 where all pixels of an image are reprojected to acommon planar depth. In some cases, the parallax correction 255 includesa full reprojection 265 where various pixels are reprojected todifferent depths.

By performing these different transforms or reprojections, theembodiments are optionally able to perform three-dimensional (3D)geometric transforms on the raw camera images to transform theperspectives of the raw images in a manner so as to correlate with theperspectives of the user's pupils 230 and 235. Additionally, the 3Dgeometric transforms rely on depth computations in which the objects inthe HMD 200's environment are mapped out to determine their depths.Based on these depth computations, the embodiments are able tothree-dimensionally reproject or three-dimensionally warp the raw imagesin such a way so as to preserve the appearance of object depth in thepassthrough image(s) 250, where the preserved object depth substantiallymatches, corresponds, or visualizes the actual depth of objects in thereal world. Accordingly, the degree or amount of the parallax correction255 is at least partially dependent on the degree or amount of theoffsets 240 and 245.

By performing the parallax correction 255, the embodiments effectivelycreate “virtual” cameras having positions that are in front of theuser's pupils 230 and 235. By way of additional clarification, considerthe position of camera 205, which is currently above and to the left ofthe pupil 230. By performing the parallax correction 255, theembodiments programmatically transform images generated by camera 205,or rather the perspectives of those images, so the perspectives appearas though camera 205 were actually positioned immediately in front ofpupil 230. That is, even though camera 205 does not actually move, theembodiments are able to transform images generated by camera 205 sothose images have the appearance as if camera 205 were positioned infront of pupil 230.

Generating Images

Attention will now be directed to FIG. 3 , which illustrates anenvironment 300 in which an HMD 305 is operating. Notice, the HMD 305includes at least a first camera 310 and a second camera 315. It isoften the case that the modality of the first camera 310 is differentthan the modality of the second camera 315. For example, the firstcamera 310 may have a low light image modality while the second camera315 may have a thermal imaging modality. Of course, other modalities(such as the ones discussed previously) may also be used.

FIG. 3 shows how the first camera 310 is scanning the environment 300,as shown by scan 320. Likewise, the second camera 315 is also scanningthe environment 300, as shown by scan 325. As a result of performing thescans, the first camera 310 generates a first image 330, and the secondcamera 315 generates a second image 335. Of course, any number of imagesmay be generated.

FIG. 4 shows a first image 400, which is representative of the firstimage 330 from FIG. 3 . The first image 400 is comprised of a set ofpixels 405, such as pixel 410. A texture (aka “intensity”) may bedetermined for each pixel or for at least a group of pixels. Forexample, the pixel 410 is shown as having a texture 415 (e.g., thetexture value “91”).

As used herein, the term “texture” generally refers to a metric or a setof values that quantify the spatial arrangement of intensity and/orcolor of a pixel in an image. Stated differently, texture characterizesthe spatial distribution of a pixel's intensity level relative to thatpixel's neighboring pixels. Texture is also used to divide or partitionan image into different so-called “regions of interest” so those regionscan then be segmented or classified. Additionally, texture reflects orquantifies other characteristics, such as smoothness, coarseness, andregularity.

FIG. 4 shows a second image 420, which is representative of the secondimage 335 of FIG. 3 . Notice, the second image 420 is also comprised ofa set of pixels 425.

Due to the placement of the cameras on the computer system (e.g., theHMD 305 of FIG. 3 ) the fields of view (FOV) of the different cameras atleast partially overlap with one another. Consequently, the resultingimages from the first and second cameras include corresponding orsimilar content. The set of common pixels 430 illustrates this concept.That is, the set of common pixels 430 are pixels representing the sameenvironmental content, which has been captured by both the first image400 and the second image 420. Accordingly, the various images includepixels and textures 435.

It should be noted that the texture values for the common pixels betweenthe two images may vary even though they reflect the same area of theenvironment. By way of example, suppose a thermal imaging camera wasdirected toward a spotlight and further suppose a low light camera wasalso directed toward that same spotlight. Here, the two resulting imageswould capture the same area of the environment, but the pixelintensities will likely be different. That is, the illumination providedby the spotlight might possibly saturate the low light camera sensorswhile that same illumination might have no effect on the sensors of thethermal imaging camera. Further details on this aspect will be providedlater. In any event, the embodiments are able to determine which pixelsof which images reflect similar or corresponding content, even if thetexture or intensity values for those pixels are different between thetwo images.

In accordance with the disclosed principles, the embodiments are able toperform edge detection 500, as shown in FIG. 5 , on each one of thedifferent images. To clarify, edge detection may be performed on thefirst image 400 and also on the second image 420 shown in FIG. 4 . Insome cases, the edge detection operations may be limited or restrainedto only those pixels that are common between the two images (e.g., theset of common pixels 430).

FIG. 5 shows a set of pixels 505A, which are representative of the setof common pixels 430 from FIG. 4 . Notice, the set of pixels 505A alsoshows the textures for the various pixels, such as texture 510A and515A.

The embodiments are able to use the various pixels to perform the edgedetection 500. For example, in the set of pixels 505B, which arerepresentative of the set of pixels 505A, notice the stark contrastbetween the texture 510B and the texture 515B. That is, the texturevalue for the texture 510B is “10” while the texture value for thetexture 515B is “87.” The variance or difference between these texturevalues surpasses a threshold value, thereby causing the embodiments toidentify or determine an “edge” exists between the respective pixels.The boldened line between the two pixels reflects this detected edge, asshown by detected edge 520.

As used herein, the term “edge” generally refers to a significant localchange that exists in an image, such as a change in the image'sintensity at a particular location. An edge can also be thought of as adiscontinuity change in the image's intensity at that particularlocation. The process of detecting edges, or “edge detection,” is thetechnique for identifying points or locations within an image where theimage's intensity or brightness changes dramatically and produces thediscontinuity.

The term “saliency” (such as saliency 525) reflects an amount of texturevariation 530 that exists between groups of pixels. In this case, thetexture variation 530 between texture 510B and 515B is the value “77,”which (in this example case) surpasses a predetermined threshold valuefor determining whether an edge exists between pixels. If the saliencyfor a group of pixels meets or exceeds the threshold value, then an edgeexists.

The embodiments are able to analyze the textures for the various pixelsin the images to determine the texture variations between the pixels.The texture variations are then used to determine the saliency of theimage and to detect the presence or absence of edges between pixels.

Turning briefly to FIG. 6 , this figure shows an edge detection 600process performed on an image 605. The result of the edge detection 600is the image 610, which illustrates the various detected edges 615.These edges 615 were detected based on the saliency and texturevariations that were identified by analyzing the image 605.

Edge detection may be performed in a number of ways. One exampletechnique is illustrated in FIG. 7 .

FIG. 7 shows an edge detection 700 technique that is based on kernelconvolution 705. Kernel convolution 705 involves the use of a kernel710, which is a defined grouping of pixels or which can also be referredto as a small matrix. In some cases, the grouping of pixels may be a“3×3” 715 group of pixels, a “5×5” 720 group of pixels, or a “7×7” 725group of pixels. Stated differently, various edge detection weights orresponses (aka alpha intensities) may be computed by kernel convolution705, where a kernel 710 of pixels used by the kernel convolution 705 iscomprised of a group of pixels.

In image processing, kernel convolution 705 is generally used for edgedetection, sharpening, blurring, or even embossing. The technique isperforming by executing a convolution between a kernel and an image.Briefly, a convolution is a technique for adding a particular element ofan image to its adjacent neighbors, weighted by the kernel. Accordingly,edges in an image may be detected by performing kernel convolution 705.

Saliency can also be performed using a variety of techniques, asillustrated in FIG. 8 via the saliency determination 800. As shown, thesaliency determination 800 includes, but is not limited to, use of aSobel filter 805 to determine saliency. A Sobel filter 805 is used inimage processing to create an image focused on edges. Generally, theSobel filter 805 is a type of discrete differentiation operator (e.g., asingle derivative filter) that computes the approximation of an imageintensity's gradient.

Optionally, the saliency determination 800 may be based on the use of aLaplacian filter 810. The Laplacian filter 810 is a type of derivativefilter designed to extract both the vertical and horizontal edges froman image, thereby causing the Laplacian filter 810 to be distinct fromthe Sobel filter 805 (i.e. a type of single derivative filter).

Optionally, saliency can be computed based on a computed variance ofintensity values for pixels included within a batch of pixels, as shownby computed variance 815. Optionally, saliency can be determined using aneural network 820 or any type of machine learning. Any type of MLalgorithm, model, machine learning, or neural network may be used toidentify edges. As used herein, reference to “machine learning” or to aML model or to a “neural network” may include any type of machinelearning algorithm or device, neural network (e.g., convolutional neuralnetwork(s), multilayer neural network(s), recursive neural network(s),deep neural network(s), dynamic neural network(s), etc.), decision treemodel(s) (e.g., decision trees, random forests, and gradient boostedtrees), linear regression model(s) or logistic regression model(s),support vector machine(s) (“SVM”), artificial intelligence device(s), orany other type of intelligent computing system. Any amount of trainingdata may be used (and perhaps later refined) to train the machinelearning algorithm to dynamically perform the disclosed operations.

The ellipsis 825 demonstrates how other techniques may also be used todetermine saliency. Accordingly, various different techniques may beused to detects edges and saliencies.

Generating an Enhanced Image

Attention will now be directed to FIG. 9 , which illustrates a flowchart 900 of an example process involving the use of images, textures,saliencies, and an alpha map to generate an enhanced image. Initially,FIG. 9 shows a first image 905, which corresponding texture 910, and asecond image 915, which corresponding texture 920. Using the principlesdiscussed earlier, the embodiments are able to determine a firstsaliency 925 for the first image 905 and a second saliency 930 for thesecond image 915.

An alpha map 935 is then generated based on the first saliency 925 andthe second saliency 930. The alpha map 935 reflects edge detectionweights 940 that have been computed for each of the common pixels thatare common between the first image 905 and the second image 915, and thecomputation is based on the first saliency 925 and the second saliency930.

Turning briefly to FIG. 10 , this figure shows an equation 1000 used togenerate an alpha map 1005, which is representative of the alpha map 935from FIG. 9 . Specifically, the equation 1000 is as follows:Alpha Map=(Saliency (B))/((Saliency (A)+Saliency (B)))

Where “Saliency(B)” is the saliency of a thermal image, or rather, thesecond saliency 925 (i.e. the saliency of the second image 905) andwhere “Saliency(A)” is the saliency of a low light image, or rather, thefirst saliency 930 (i.e. the saliency of the first image 915).

The alpha map 1005 is then shown as comprising a number of pixels, suchas pixel 1010. Each pixel is assigned its own corresponding alphaintensity 1015 (i.e. an “edge detection weight” such as the edgedetection weights 940 in FIG. 9 ), which is a number between 0 and 1 andwhich is a number that is generated using the equation 1000. An alphaintensity value of 0 indicates an alpha intensity originating only fromthe second image (e.g., the thermal image), and a value of 1 indicatesan alpha intensity originating only from the first image (e.g., the lowlight image). An alpha intensity value of 0.5 indicates an alphaintensity originating from both the first (e.g., the low light image)and second image (e.g., the thermal image) in equal parts.

The alpha intensities, or rather the edge detection weights 940 in FIG.9 , are used to determine how much texture from each respective imagewill be used to when generating an enhanced image. By way of example, analpha intensity of 1 (or an edge detection weight of 1) indicates thattexture originating only from the first image (e.g., the low lightimage) will be used and no texture from the second image (e.g., thethermal image) will be used. Relatedly, an alpha intensity of 0indicates that texture originating only from the second image (e.g., thethermal image) will be used and no texture from the first image (e.g.,the low light image) will be used. An alpha intensity of 0.5 meanstextures from both images will be used equally.

In more detail, let “I” be the image of the second modality (e.g.,modality 2) and “J” be the image of the first modality (e.g., modality1). Image “I” is divided into both a low frequency component (e.g.,“I_l”) and a high frequency component and (“I_h”). The low frequencycomponent (“I_l”) is derived by applying a box filter on “I.”

The high frequency component (“I_h”) is computed by subtracting “I_l”from the original image “I.” In other words, “I_h”=“I”−“I_l.”

In the same manner, the image J is decomposed into low and highfrequency components (e.g., “J_l” and “J_h”, respectively). Two alphamaps are then computed (e.g., “alpha_l” for the low frequency images and“alpha_h” for the high frequency images).

The final fused image “F” (aka an enhanced image) is obtained via thefollowing equation:F=(1−alpha_l)*I_l+alpha_l*J_l+(1−alpha_h)*I_h+alpha_h*J_h.

Computing alpha_l and alpha_r is performed by computing image saliency.For computing the low frequency saliency maps, the embodiments ignoreimage details and focus on the dominant image edges. The saliency mapS_I_l is computed for the low frequency component of image I by runningthe following steps:

As a first step, the embodiments scale down the original image I twice.In other words, the image is scaled from (as one example) a 640×480resolution to a 420×240 resolution and then to a 210×120 resolution.

As a second step, the embodiments apply a Sobel filter on the scaledimage. As a third step, the embodiments apply a Gaussian filter on theSobel image. S_I_l is obtained by upscaling the filtered image twice.For example, the image is upscaled from a 210×120 resolution to a420×240 resolution to a 640×480 resolution.

The saliency map S_J_l for the low frequency component of image J iscomputed as above with the only difference being that image J is used asan input to the saliency computation. The alpha map (e.g., alpha map 935from FIG. 9 ) is then computed as follows (thereby reflecting equation1000 from FIG. 10 ):alpha_l=S_J_l/(S_I_1+S_J_1).

For computing the high frequency saliency maps, the embodiments do focuson image details. The saliency maps S_I_h and S_J_h for the highfrequency components of images I and J are computed running the sameprocedure as above without applying image scaling. In other words, onlysteps 2 and 3 are run. Finally, the high frequency alpha map (e.g.,alpha map 935 from FIG. 9 ) is computed by following the equation listedbelow (thereby reflecting equation 1000 from FIG. 10 ):alpha_h=S_J_h/(S_I_h+S_J_h).

In this regard, the alpha map 935 from FIG. 9 may comprise multiplealpha maps. Such alpha maps may include an alpha map for a highfrequency computation and an alpha map for a low frequency computation.The equation for calculating the final fused image “F” may then rely onthese various alpha maps to determine the amount of texture to use fromeach image.

Returning to FIG. 9 , the flow chart 900 then illustrates adetermination phase 945 where the embodiments determine how much texturefrom the first image 905 (e.g., the low light image) and/or the secondimage 915 (e.g., the thermal image) to use to generate an enhanced image950. Such determinations are based on the edge detection weights 940(i.e. the alpha intensities) included in the alpha map 935. FIGS. 11A,11B, 11C, and 11D provide additional clarification relative to thedisclosure presented in FIGS. 9 and 10 .

In some implementations, the first image 905 (e.g., the low light image)and the second image 915 (e.g., the thermal image) are aligned so thattheir corresponding perspectives match or coincide with one another.This alignment process may be performed by the parallax correctionprocesses mentioned earlier. In some cases, alignment may also beperformed by matching feature points that are presented between the twoimages. A “feature point” is considered a point of interest thatprovides a clear contrast, such as a corner or an edge. The embodimentsare able to align images by identifying and then matching common featurepoints that are present in both the images.

FIG. 11A shows an example of a first image 1100, which is a low lightimage generated by a camera having a low light modality. The variouslighting signatures (or infrared (IR) signatures) of the objects arerepresented by the different grey tones in the image. Notice, aspotlight is attached to the edge of the building and is illuminating anarea of the walk where, as will be shown in FIG. 11B, a human isstanding. The illuminated area has saturated the low light camera,resulting in the saturated region 1105 of the first image 1100.

FIG. 11B, on the other hand, shows an example of a second image 1125,which is thermal image generated by a camera having a thermal modality.The various heat signatures of the objects are represented by thedifferent grey tones in the image. For example, the second image 1125shows a human standing on a walk, as shown by thermal content 1130. Thatis, the human that was previously not visible in the first image 1100and that was standing in the illuminated area is now visible in thesecond image 1125.

Other features that were visible in the first image 1100 (e.g., content1110, 1115, and 1120) are now not visible in the second image 1125because those objects do not have a heat signature. To clarify, thosebushes did not have a heat signature and thus were not visible in thethermal image (i.e. the second image 1125) but were visible in the lowlight image (i.e. the first image 1100).

Accordingly, the thermal image beneficially visualizes some content thatmay not be visible in a low light image, and the low light imagebeneficially visualizes some content that may not be visible in thethermal image. In accordance with the disclosed principles, it isdesirable to generate an enhanced image that provides the benefits ofboth the low light image and the thermal image. Stated differently, itis desirable to generate an enhanced image that provides the benefitsfrom different images that were generated from different cameras ofdifferent modalities.

Following the flow chart 900 described in FIG. 9 , the embodiments areable to generate an alpha map 1135, as shown in FIG. 11C. The alpha map1135 indicates the source or contributor that provided texture for aparticular pixel.

Specifically, the dark pixels in the alpha map 1135 reflect texture orpixel content that is sourced from one camera (e.g., the low lightcamera) while the white pixels reflect texture or pixel content that issourced from a different camera (e.g., the thermal camera). Toillustrate, supposing the dark pixels came from a low light camera andthe white pixels came from a thermal camera, one can compare the alphamap 1135 against the first image 1100 of FIG. 11A and the second image1125 of FIG. 11B. Notice, for areas where the low light camera wassaturated, the alpha map 1135 shows white pixels; meaning that thosepixels were sourced from the thermal camera. Similarly, for areas thatare black in the alpha map 1135, those pixels were sourced from the lowlight camera. Some areas of the alpha map may include merged content;meaning that those pixels were sourced from a combination of both thelow light camera and the thermal camera (or other camera modalities).

By following the flow chart 900 of FIG. 9 , the embodiments are able togenerate an enhanced image 1140, as shown in FIG. 11D. Notice, theenhanced image 1140 provides the benefits obtained from using a thermalimage and, simultaneously, provides the benefits obtained from using alow light image. To illustrate, in the enhanced image 1140, the content1145 (i.e. the human) is now visible using pixels obtained from thethermal image. Similarly, the content 1150, 1155, and 1160 (i.e. thebushes) are now visible using pixels obtained from the low light image.Such pixels are determined based on the embodiments' abilities to detectedges, as described earlier, using the texture values. If a set of edgesare visible in one image (e.g., perhaps the thermal image) but thosesame set of edges are not visible in a different image (e.g., perhapsthe low light image), it suggests that the thermal camera was able topick up or detect content that was not visible by the low light camera,and vice versa.

By way of an additional explanation, the pixels in the alpha mapcorresponding to the illuminated area (i.e. the area where the spotlightis shining) likely reflected alpha intensities equal to or approximatingthe value 0, meaning that almost all of the texture used in the enhancedimage 1140 for the illuminated area came from the thermal image.Similarly, the pixels in the alpha map corresponding to the busheslikely reflected alpha intensities equal to or approximating the value1, meaning that almost all of the texture used in the enhanced image1140 for the bush areas came from the low light image. The textures ofthe other areas of the enhanced image 1140 (e.g., the building) likelycame from both the thermal image and the low light image, and theproportion of texture used depends on the alpha intensities in the alphamap.

In some cases, a color coding scheme may be used to reflect theorigination of texture for a pixel. For instance, texture obtained fromthe first image can have a particular color hue associated with it whiletexture obtained from the second image can have a different color hueassociated with it. When texture is obtained from both images, then theresulting hue can be the combination of the two hues, and the resultinghue is based on the proportion of texture provided by each image. Eachpixel may also be tagged with metadata to reflect the source of thepixel's texture.

As another example, suppose the embodiments were being used in an indoorenvironment where walls were present. In one example case, suppose a setof hot water pipes were located within the wall. The low light camerawould reflect the walls, but the thermal imaging camera would detect aheat signature. By generating the enhanced image, a user will be able todetect the presence of the hot water pipes even though those would notnormally be visible via the naked eye.

In some scenarios, noise may be present in one of the images. Forinstance, in very low light conditions (e.g., 1.0 millilux or“starlight” environments), there might not be enough light photons inthe environment for the low light camera to generate a high qualityimage. Indeed, the resulting image generated by the low light camera maybe heavily corrupted with noise. FIG. 12 is illustrative.

When used in a very low light environment (e.g., about 1.0 millilux or“starlight” environments), the low light camera sensors attempt tocompensate for the low light condition by ramping up the camera's gain(e.g., digital gain, analog gain, or a combination of digital and analoggain). As a result of ramping up the camera sensor's gain, the resultingimage is very noisy, as shown by the second image 1200 of FIG. 12 .Specifically, one can observe the noise 1205 present in the second image1200.

As described above, there is a camera characteristic 1210 associatedwith the camera. This camera characteristic 1210 includes a gain 1215(e.g., an analog gain 1220 or a digital gain 1225). Additionally, thiscamera characteristic 1210 can include an exposure 1230 time of thecamera. In low light conditions, the gain 1215 and/or the exposure 1230can be increased in an effort to detect more photons. Unfortunately,when this occurs in very low light conditions, the result of modifyingthe camera characteristic 1210 results in a high level of noise 1205.

FIG. 13 shows what would occur if the edge detection process describedin FIG. 5 were implemented using the second image 1200 of FIG. 12 .Specifically, FIG. 13 shows an edge detection 1300 process that is beingperformed on a set of pixels 1305. The set of pixels 1305 are pixelsincluded in the second image 1200 of FIG. 12 .

Notice, the texture values of the various pixels vary or changedramatically. These changes are due to the noise 1205 in the image, asshown by noise 1310. To illustrate, the texture 1315 has a value of “65”while the adjacent texture 1320 has a value of “12.” By executing theedge detection algorithm mentioned earlier, the algorithm would identifya detected edge 1325.

Similarly, the algorithm would detect numerous other edges between thepixels in the set of pixels 1305, as shown by the bolded black linesseparating some of the various pixels. As a result of the noise 1310,the algorithm would detect a very high number of edges in the image. Thehigh number of edges would lead to a high saliency value for that image.With that high saliency value, the highly noisy image would then beweighted or considered more heavily than the other image (e.g., thethermal image). Consequently, when the alpha map is generated, the noisyimage will be weighted more, and the resulting fused image will also behighly noisy and of low quality. What is needed, therefore, is atechnique to mitigate the effects of noise that may be present in oneimage.

In accordance with the disclosed principles, the embodiments introduce anew “scaling factor” that is applied to the texture values of an imagein order to mitigate the effects of noise in that image. Notably, the“scaling” or “scale” factor is different than a gain setting of thefirst camera. FIG. 14 is illustrative.

FIG. 14 shows an example process 1400 where a pixel intensity 1405 (akaa pixel texture) is scaled via use of a scale factor 1410 in order tomitigate the effects of noise. For instance, the pixel intensity 1405can be the value “65” for the texture 1315 in FIG. 13 . That value isscaled using the scale factor 1410 (aka scaling factor) to generate ascaled intensity value or a scaled texture value for that particularpixel. All pixels, or perhaps a selected number of pixels in the image,can be scaled using the scale factor 1410. Typically, the same scalefactor 1410 is used for all of the pixels that are being scaled, thoughin some cases, a different scale factor can be used for different pixelsin the set.

The scale factor 1410 can be acquired from a lookup table 1415 or usinga closed form solution 1420 (e.g., a model). Furthermore, the scalefactor 1410 is at least partially dependent on the camera characteristic1210 mentioned in FIG. 12 (e.g., the gain 1215 and/or the exposure1230).

Using the lookup table 1415 as an example, a mapping lookup table can beprovided that maps values of the camera characteristic withcorresponding values of the scale factor. As an example, say thecamera's gain is a value from 1 to 32. The lookup table can map one or acombination of more than one gain values to corresponding scale factors.Notably, it is often the case that the gain and scale factors areinversely proportional in that the scale factor effectively undoes theincrease in gain. One will appreciate how any mapping can be used, wherethe mapping can be based on a calibration event that is performed todetermine the mapping values. The calibration event can be at leastpartially dependent on the amount of ambient light in the environment.

In effect, applying the scale factor 1410 to the pixel intensity 1405operates to normalize the pixel intensity 1405. Often, gain settings arebetween 1 and 32. Often, the scale or scaling factor is between 0 and20. Thus, it is often the case that the scaling factor is a differentvalue than a value of a gain setting of the first camera.

Instead of a lookup table 1415, the embodiments can also rely on aclosed form solution 1420 (aka a closed form expression) to determinewhich scale factor 1410 to apply to the pixel intensities or textures.Generally, a closed form expression is a mathematical expression thatuses a finite number of operations to achieve a solution. Moregenerally, any type of model can be used as a closed form solution inorder to determine which scale factor 1410 to use, where the scalefactor 1410 is dependent on the camera characteristic 1210 of FIG. 12 .As a result of applying the scale factor 1410 to the pixel intensity1405, a resulting scaled intensity 1425 is produced. FIG. 15 providessome additional detail.

FIG. 15 shows an edge detection 1500 process that is similar to thatedge detection processes mentioned previously. Before, in the scenariowhere a scale factor was not used, the scenario presented in the left ofFIG. 15 was produced, resulting in a high number of edges being detected(e.g., edges detected 1505 as shown by the bolded black lines) becauseof the high level of noise. By applying the scale factor (e.g., as shownby apply scale factor 1510), the pixel intensities or textures in theimage are scaled or “normalized,” thereby mitigating the effects ofhaving a high gain setting or high exposure period.

To illustrate, in this example scenario, a scale factor of “20” wasselected based on the camera's characteristic (e.g., gain or exposure).Here, each of the pixel intensities were divided by the scale factor togenerate scaled intensities. For example, starting from the top leftcorner of the left set of pixels, the intensity “10” was divided by “20”to generate a scaled value of “0.5.” Similarly, the intensity “91” wasdivided by “20” to generate a scaled value of “4.55,” and so on and soforth.

Now, the disparity between the pixel intensities may not be sufficient(or may not exceed a threshold) for the edge detection algorithm todetermine an edge exists between those different pixels. For instance,in this example scenario, no edges were detected, as shown by no edgesdetected 1520.

As a result of fewer edges being detected in the one image (e.g., thelow light image), the resulting saliency for that image will also belower. Because of a lower saliency, the resulting alpha map will weightthat image lower than the other image (e.g., the thermal image), asshown by the alpha map 1525. Notice, the alpha intensities for thevarious pixels (e.g., pixel 1530 has an alpha intensity 1535) are closerto a value of “0” than to a value of “1.” Values closer to “0” in thealpha map 1525 indicate that the alpha map is weighting the second image(e.g., the thermal image) more than the first image (e.g., the low lightimage). Stated differently, when generating the fused enhanced image,more texture content will be pulled from the thermal image than from thelow light image. This is beneficial because the low light image ishighly noisy.

Example Methods

The following discussion now refers to a number of methods and methodacts that may be performed. Although the method acts may be discussed ina certain order or illustrated in a flow chart as occurring in aparticular order, no particular ordering is required unless specificallystated, or required because an act is dependent on another act beingcompleted prior to the act being performed.

Attention will now be directed to FIGS. 16A and 16B, which illustrate aflowchart of an example method 1600 for generating a fused enhancedimage. In some cases, the method 1600 may be implemented by a computersystem or by a HMD that includes a first camera of a first modality(e.g., a low light modality) and a second camera of a second modality(e.g., a thermal modality). In some cases, one of the first modality orthe second modality is a short wave infrared (SWIR) modality or,alternatively, a near infrared (NIR) modality.

Optionally, the first modality is selected from a group of modalitiescomprising: a visible light modality, a monochrome modality, a nearinfrared (NIR) modality, a short wave infrared (SWIR) modality, athermal modality, or an ultraviolet (UV) modality. As another option,the second modality is also selected from the group of modalities and isdifferent than the first modality.

Method 1600 involves an act (act 1605) of generating a first image(e.g., first image 1100 of FIG. 11A) of an environment using the firstcamera of the first modality (e.g., low light modality). There is alsoan act (act 1610) of generating a second image (e.g., second image 1125of FIG. 11B) of the environment using the second camera of the secondmodality (e.g., thermal imaging modality). These two acts may beperformed in parallel with one another or in serial with one another.For example, in the case where the acts are performed in serial, act1605 may occur first and then act 1610 or, alternatively, act 1610 mayoccur first and then act 1605. Notably, the two images at leastpartially reflect the same content or the same area of the environment.

In some implementations, parallax correction is performed on one or moreof the first image or the second image. The parallax correction isperformed to align the perspectives embodied within the two images, asdiscussed earlier. The parallax correction can involve performing aplanar reprojection or, alternatively, a full reprojection. In somecases, correcting for parallax results in the perspective of the twoimages being aligned with the person's pupils or, alternatively, withanother desired perspective that is different from the person's pupilperspective.

For example, it may be the case that the second image is parallaxcorrected to conform with the perspective embodied by the first image.Alternatively, it may be the case that the first image is parallaxcorrected to conform with perspective embodied by the second image.Indeed, various different perspectives may be achieved by performingparallax correction. Accordingly, a planar reprojection operation or afull reprojection operation may be performed to account for parallax.

Act 1615 involves identifying pixels that are common between the firstimage and the second image. For example, the set of common pixels 430from FIG. 4 can be identified. Such an operation is beneficial so thatprocessing is performed on relevant content, or rather, on content thatis supposedly the same between the two images. Furthermore, such anoperation is performed in order to offset or account for scenarios suchas the one described earlier, where one image may not include adequatetexture but where the other image's texture can compensate for such adeficiency. As discussed earlier, it may be the case that even thoughthe pixels are supposed to represent the same content, the resultingtextures between the two images may be different.

Act 1620 involves determining a first set of textures for the commonpixels included in the first image. In parallel or in serial with act1620, act 1625 involves determining a second set of textures for thecommon pixels included in the second image. For example, the textures435 from FIG. 4 can be determined for both images.

Act 1630 involves identifying a camera characteristic of the firstcamera, where the camera characteristic is associated or linked withnoise that may be present in the first image. For instance, the cameracharacteristic mentioned in act 1630 may be the camera characteristic1210 of FIG. 12 , which includes a gain 1215 (e.g., analog and/ordigital) or an exposure 1230. Both of these characteristics are linkedor associated with the amount of noise that will be present in the firstimage. For instance, a higher gain setting or a prolonged exposure willboth indicate that more noise will be present in the resulting image.Stated differently, in some cases, the camera characteristic is a gainsetting of the first camera. A relatively higher value for the gainsetting (and exposure setting too) indicates a relatively higher amountof noise will likely be in the first image. Relatedly, a relativelylower value for the gain setting (and exposure setting) indicates arelatively lower amount of noise will likely be in the first image.

Based on the identified camera characteristic (e.g., gain or exposure),there is an act (act 1635) of applying a scaling factor to the first setof textures to generate a scaled set of textures. The process ofapplying the scaling factor operates to mitigate an effect of noise thatis potentially present in the first image. In some implementations, ascaling factor (e.g., either the same scaling factor or a differentscaling factor) is applied to the second set of textures for the commonpixels in the second image. Consequently, the second image is alsonormalized, similar to the first image. Scaling the second image isbeneficial in situations such as when one camera is a visible lightcamera and the other camera is an IR camera.

In some cases, the scaling factor is obtained from a lookup tablecomprising values that were generated based on a calibration event. Insome cases, the scaling (or scale) factor is obtained from a model, suchas a closed form solution/expression. In some embodiments the process ofapplying the scaling factor includes dividing (e.g., for each texture inthe first set of textures) each texture by the scaling factor togenerate a scaled texture value. Of course, if a different type ofscaling factor were used (e.g., perhaps a decimal value), then applyingthe scaling factor can be performed via multiplication instead ofdivision. Often, the scaling factor is relatively lower when theenvironment has a relatively higher amount of ambient light, and thescaling factor is relatively higher when the environment has arelatively lower amount of ambient light.

In some cases, applying the scaling factor is performed in a response toa determination that an ambient light level of the environment is belowa light threshold. For instance, use of the scaling factor can betriggered based on the ambient light levels. If the ambient light levelsare above a threshold level, then no scaling might occur. The scalingmight occur after the ambient light levels drop below a certain level.Alternatively, the scaling factor might always be performed, regardlessof the ambient light level. For instance, it may be the case that thescaling factor can be set to a value of “1” for certain gain or exposuresettings. Division by “1” results in the original number; therefore,applying the scaling factor has no effect on the resulting image oralpha map.

Method 1600 continues in FIG. 16B. Specifically, method 1600 includes anact (act 1640) of using the scaled set of textures to determine a firstsaliency of the first image. Notably, the first saliency reflects anamount of texture variation that is present in the scaled set oftextures.

In parallel or in serial with act 1640, act 1645 includes determining asecond saliency of the second image. The second saliency reflects anamount of texture variation in the second image. For example, thesaliency 525 from FIG. 5 may be determined for both images or at leastfor the pixels that are common between the two images. Optionally, thefirst saliency and the second saliency are computed using one or moreof: a Sobel filter, a Laplacian filter, a neural network, or acomputation based on intensity variation in a group of pixels.

Method 1600 includes an act (act 1650) of generating an alpha map thatreflects edge detection weights, or “alpha intensities,” that have beencomputed for each one of the common pixels based on the first saliencyand the second saliency. For example, the alpha map 1525 from FIG. 15can be computed based on the saliencies in order to reflect the edgedetection weights (e.g., alpha intensity 1535). As discussed earlier,the alpha map is computed based on the second saliency divided by thesum of the first saliency and the second saliency. Therefore, as aresult of the scaled set of textures being scaled and as a result of thealpha map being generated based on the first saliency (which is based onthe scaled set of textures), the alpha map weights the second saliencymore heavily than the first saliency.

Based on the alpha map, there is then an act (act 1655) of mergingtextures from the common pixels included in the first image and thesecond image to generate a fused enhanced image. That is, the alpha mapis used to determine how much texture from the first image and/or fromthe second image to use to generate the fused enhanced image.

This determining process is based on the edge detection weights includedwithin the alpha map. With reference to FIGS. 11A and 11B, the texturefor the content 1110 is visible in the first image 1100 but is notvisible in the second image 1125. Consequently, the embodiments willselect the texture for the content 1110 from the first image 1100.Similarly, the texture for the thermal content 1130 is visible in thesecond image 1125 but is not visible in the first image 1100.Consequently, the embodiments will select the texture for the thermalcontent 1130 from the second image 1125. The alpha map is used to makethese determinations because the alpha map indicates or reflects thepresence or absence of edges in a pixel via the edge detection weights.

In some cases, the fused enhanced image is then displayed on a displayof the HMD or computer system as a passthrough image. A user can thenview the passthrough image via the display.

In some cases, the fused enhanced image is further analyzed so thatobject recognition or object segmentation is performed on the enhancedimage. For example, in the scenario where the embodiments are used in aself-driving car, the car may have at least two different cameras ofdifferent modalities. The car's system can operate in the mannerdescribed above. When the fused enhanced image is finally generated, thecar's system can then analyze the image to identify objects, such as toperform obstacle avoidance or to ensure the car is traveling in adesired path. Therefore, in some situations, the fused enhanced imagemay (or may not) be displayed and may (or may not) be further analyzedin an effort to identify objects for obstacle avoidance.

Example Computer/Computer Systems

Attention will now be directed to FIG. 17 which illustrates an examplecomputer system 1700 that may include and/or be used to perform any ofthe operations described herein. Computer system 1700 may take variousdifferent forms. For example, computer system 1700 may be embodied as atablet 1700A, a desktop or a laptop 1700B, a wearable device (e.g., HMD1700C), a mobile device, a standalone device, or any other device asillustrated by the ellipsis 1700D. Computer system 1700 may also be adistributed system that includes one or more connected computingcomponents/devices that are in communication with computer system 1700.

In its most basic configuration, computer system 1700 includes variousdifferent components. FIG. 17 shows that computer system 1700 includesone or more processor(s) 1705 (aka a “hardware processing unit”) andstorage 1710.

Regarding the processor(s) 1705, it will be appreciated that thefunctionality described herein can be performed, at least in part, byone or more hardware logic components (e.g., the processor(s) 1705). Forexample, and without limitation, illustrative types of hardware logiccomponents/processors that can be used include Field-Programmable GateArrays (“FPGA”), Program-Specific or Application-Specific IntegratedCircuits (“ASIC”), Program-Specific Standard Products (“ASSP”),System-On-A-Chip Systems (“SOC”), Complex Programmable Logic Devices(“CPLD”), Central Processing Units (“CPU”), Graphical Processing Units(“GPU”), or any other type of programmable hardware.

As used herein, the terms “executable module,” “executable component,”“component,” “module,” or “engine” can refer to hardware processingunits or to software objects, routines, or methods that may be executedon computer system 1700. The different components, modules, engines, andservices described herein may be implemented as objects or processorsthat execute on computer system 1700 (e.g. as separate threads).

Storage 1710 may be physical system memory, which may be volatile,non-volatile, or some combination of the two. The term “memory” may alsobe used herein to refer to non-volatile mass storage such as physicalstorage media. If computer system 1700 is distributed, the processing,memory, and/or storage capability may be distributed as well.

Storage 1710 is shown as including executable instructions 1715. Theexecutable instructions 1715 represent instructions that are executableby the processor(s) 1705 of computer system 1700 to perform thedisclosed operations, such as those described in the various methods.

The disclosed embodiments may comprise or utilize a special-purpose orgeneral-purpose computer including computer hardware, such as, forexample, one or more processors (such as processor(s) 1705) and systemmemory (such as storage 1710), as discussed in greater detail below.Embodiments also include physical and other computer-readable media forcarrying or storing computer-executable instructions and/or datastructures. Such computer-readable media can be any available media thatcan be accessed by a general-purpose or special-purpose computer system.Computer-readable media that store computer-executable instructions inthe form of data are “physical computer storage media” or a “hardwarestorage device.” Computer-readable media that carry computer-executableinstructions are “transmission media.” Thus, by way of example and notlimitation, the current embodiments can comprise at least two distinctlydifferent kinds of computer-readable media: computer storage media andtransmission media.

Computer storage media (aka “hardware storage device”) arecomputer-readable hardware storage devices, such as RAM, ROM, EEPROM,CD-ROM, solid state drives (“SSD”) that are based on RAM, Flash memory,phase-change memory (“PCM”), or other types of memory, or other opticaldisk storage, magnetic disk storage or other magnetic storage devices,or any other medium that can be used to store desired program code meansin the form of computer-executable instructions, data, or datastructures and that can be accessed by a general-purpose orspecial-purpose computer.

Computer system 1700 may also be connected (via a wired or wirelessconnection) to external sensors (e.g., one or more remote cameras) ordevices via a network 1720. For example, computer system 1700 cancommunicate with any number devices or cloud services to obtain orprocess data. In some cases, network 1720 may itself be a cloud network.Furthermore, computer system 1700 may also be connected through one ormore wired or wireless networks 1720 to remote/separate computersystems(s) that are configured to perform any of the processingdescribed with regard to computer system 1700.

A “network,” like network 1720, is defined as one or more data linksand/or data switches that enable the transport of electronic databetween computer systems, modules, and/or other electronic devices. Wheninformation is transferred, or provided, over a network (eitherhardwired, wireless, or a combination of hardwired and wireless) to acomputer, the computer properly views the connection as a transmissionmedium. Computer system 1700 will include one or more communicationchannels that are used to communicate with the network 1720.Transmissions media include a network that can be used to carry data ordesired program code means in the form of computer-executableinstructions or in the form of data structures. Further, thesecomputer-executable instructions can be accessed by a general-purpose orspecial-purpose computer. Combinations of the above should also beincluded within the scope of computer-readable media.

Upon reaching various computer system components, program code means inthe form of computer-executable instructions or data structures can betransferred automatically from transmission media to computer storagemedia (or vice versa). For example, computer-executable instructions ordata structures received over a network or data link can be buffered inRAM within a network interface module (e.g., a network interface card or“NIC”) and then eventually transferred to computer system RAM and/or toless volatile computer storage media at a computer system. Thus, itshould be understood that computer storage media can be included incomputer system components that also (or even primarily) utilizetransmission media.

Computer-executable (or computer-interpretable) instructions comprise,for example, instructions that cause a general-purpose computer,special-purpose computer, or special-purpose processing device toperform a certain function or group of functions. Thecomputer-executable instructions may be, for example, binaries,intermediate format instructions such as assembly language, or evensource code. Although the subject matter has been described in languagespecific to structural features and/or methodological acts, it is to beunderstood that the subject matter defined in the appended claims is notnecessarily limited to the described features or acts described above.Rather, the described features and acts are disclosed as example formsof implementing the claims.

Those skilled in the art will appreciate that the embodiments may bepracticed in network computing environments with many types of computersystem configurations, including personal computers, desktop computers,laptop computers, message processors, hand-held devices, multi-processorsystems, microprocessor-based or programmable consumer electronics,network PCs, minicomputers, mainframe computers, mobile telephones,PDAs, pagers, routers, switches, and the like. The embodiments may alsobe practiced in distributed system environments where local and remotecomputer systems that are linked (either by hardwired data links,wireless data links, or by a combination of hardwired and wireless datalinks) through a network each perform tasks (e.g. cloud computing, cloudservices and the like). In a distributed system environment, programmodules may be located in both local and remote memory storage devices.

The present invention may be embodied in other specific forms withoutdeparting from its characteristics. The described embodiments are to beconsidered in all respects only as illustrative and not restrictive. Thescope of the invention is, therefore, indicated by the appended claimsrather than by the foregoing description. All changes which come withinthe meaning and range of equivalency of the claims are to be embracedwithin their scope.

What is claimed is:
 1. A method for mitigating effects of noise whenfusing multiple images together to generate an enhanced image, saidmethod comprising: generating a first image of an environment using afirst camera of a first modality; generating a second image of theenvironment using a second camera of a second modality; identifyingpixels that are common between the first image and the second image;determining a first set of textures for the common pixels in the firstimage; determining a second set of textures for the common pixels in thesecond image; identifying a camera characteristic of the first camera,wherein the camera characteristic is associated with noise that may bepresent in the first image; based on the identified cameracharacteristic, applying a scaling factor to the first set of texturesto generate a scaled set of textures, wherein applying the scalingfactor operates to mitigate an effect of noise that is potentiallypresent in the first image; using the scaled set of textures todetermine a first saliency of the first image, wherein the firstsaliency reflects an amount of texture variation that is present in thescaled set of textures; determining a second saliency of the secondimage, wherein the second saliency reflects an amount of texturevariation that is present in the second set of textures; performing edgedetection on the first image and the second image, wherein the edgedetection is performed using only the common pixels that were identifiedin the first image and the second image; generating an alpha map thatreflects edge detection weights that have been computed for each one ofthe common pixels, wherein the alpha map is based on the first saliencyand the second saliency, and wherein the edge detection weights aregenerated based on the edge detection; and based on the alpha map,merging textures from the common pixels included in the first image andthe second image to generate a fused enhanced image.
 2. The method ofclaim 1, wherein the first modality is one of a monochrome modality, alow light modality, or a red, green, blue (RGB) modality.
 3. The methodof claim 1, wherein the second modality is a thermal imaging modality,and wherein the scaling factor is applied to the second set of texturesfor the common pixels in the second image such that the second image isalso normalized.
 4. The method of claim 1, wherein the scaling factor isobtained from a lookup table comprising values that were generated basedon a calibration event.
 5. The method of claim 1, wherein the scalingfactor is obtained based on a closed form solution.
 6. The method ofclaim 1, wherein applying the scaling factor to the first set oftextures to generate the scaled set of textures includes: for eachtexture in the first set of textures, dividing said each texture by thescaling factor to generate a scaled texture value.
 7. The method ofclaim 1, wherein the camera characteristic is based on one or more of ananalog gain setting of the first camera, a digital gain setting of thefirst camera, or an exposure time of the first camera such that thescale factor is dependent on one or more of the analog gain, the digitalgain, or the exposure time.
 8. The method of claim 1, wherein applyingthe scaling factor is performed in response to a determination that anambient light level of the environment is below a light threshold or,alternatively, applying the scaling factor is performed regardless ofthe ambient light level.
 9. The method of claim 1, wherein, as a resultof the scaled set of textures being scaled and as a result of the alphamap being generated based on the first saliency, which is based on thescaled set of textures, the alpha map weights the second saliency moreheavily than the first saliency.
 10. The method of claim 1, wherein thescaling factor is a different value than a value of a gain setting ofthe first camera.